Feature selection for surface defect classification of extruded aluminum profiles

By Apostolos Chondronasios, Ivan Popov, Ivan N. Jordanov. 2016

80 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/58258fdcdee57410c20ce33980fea494b5bdda40

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiao Fu
2. Xiaoming Lv
3. Xian Tao
4. Lin Gan
5. Xilong Liu
6. Dapeng Zhang
7. Fajie Duan
8. Jia-jia Jiang

Citations in the corpus

• Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network (2020, background): “There are lots of other features, such as co-occurrence matrix (GLCM) [23] and some grayscale statistical features [24,25].”
• Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks (2018, background): “(1) GLCM [7]: this feature is the classical texture feature, which includes four typical descriptions: energy, contrast, entropy, and correlation.”